Construction and analysis of mRNA, miRNA, lncRNA, and TF regulatory networks reveal the key genes in prostate cancer

Purpose: Prostate cancer (PCa) causes a common male urinary system malignant tumour, and the molecular mechanisms of PCa remain poorly understood. This study aims to investigate the underlying molecular mechanisms of PCa with bioinformatics. Methods: Original gene expression profiles were obtained from the GSE64318 and GSE46602 datasets in the Gene Expression Omnibus (GEO). We conducted differential screens of the expression of genes (DEGs) between two groups using the R software limma package. The interactions between the differentially expressed miRNAs, mRNAs and lncRNAs were predicted and merged with the target genes. Co-expression of the miRNAs, lncRNAs and mRNAs were selected to construct the mRNA-miRNA and-lncRNA interaction networks. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the DEGs. The protein-protein interaction (PPI) networks were constructed, and the transcription factors were annotated. The expression of hub genes in the TCGA datasets was verified to improve the reliability of our analysis. Results: The results demonstrated that 60 miRNAs, 1578 mRNAs and 61 lncRNAs were differentially expressed in PCa. The mRNA-miRNA-lncRNA networks were composed of 5 miRNA nodes, 13 lncRNA nodes, and 45 mRNA nodes. The DEGs were mainly enriched in the nuclei and cytoplasm and were involved in the regulation of transcription, related to sequence-specific DNA binding, and participated in the regulation of the PI3K-Akt signalling pathway. These pathways are related to cancer and focal adhesion signalling pathways. Furthermore, we found that 5 miRNAs, 6 lncRNAs, 6 mRNAs and 2 TFs play important regulatory roles in the interaction network. The expression levels of EGFR, VEGFA, PIK3R1, DLG4, TGFBR1 and KIT were significantly different between PCa and normal prostate tissue. Conclusion: Based on the current study, large-scale effects of interrelated mRNAs, miRNAs, lncRNAs, and TFs were revealed and a model for predicting the mechanism of PCa was provided. This study provides new insight for the exploration of the molecular mechanisms of PCa and valuable clues for further research.

Prostate cancer (PCa) involves a common male urinary system malignant tumour that has the highest incidence among European and American populations [1,2]. This disease not only seriously affects the quality of life of patients but is also associated with financial burdens for the society and family [3,4].  [5,6]. As an important factor in gene transcription and post-transcription regulation, TFs are also involved in control with miRNAs [7,8]. However, further research is needed to determine the regulation mechanisms of miRNAs, lncRNAs, TFs and mRNAs for PCa.
Microarray analysis can quickly identify all of the genes that are expressed at the same time-point [9]. Future research could benefit from the integration and analysis of the data [10]. In this work, we identified differentially expressed genes (DEGs) in prostate cancer from the GSE64318 and GSE46602 datasets. We performed Gene Ontology and signalling pathway enrichment analyses for differentially expressed miRNAs targeting mRNAs. Furthermore, we analysed the mRNAs-miRNAs-lncRNAs and protein-protein interactions (PPIs) network to reveal the interactions and identified some factors that may be associated with regulatory mechanisms in PCa. Finally, we analysed the hub genes based on PPI network and TCGA datasets. This study will contribute to the exploration of the molecular mechanism of prostate cancer and provide valuable clues for further research.

Raw data
The datasets used in the present study were downloaded from the National Center of Biotechnology TCGA is a platform for researchers to download and assess free public datasets (https://cancergenome.nih.gov/) [24]. In the present study, we verified the expression of hub genes in TCGA datasets to improve the reliability of our analysis.

Identification of Differentially Expressed mRNAs, miRNAs and lncRNAs
The results show that 60 miRNAs, 1578 mRNAs and 128 lncRNAs were differentially expressed in prostate cancer (Tables S1,S2) . The analysis of GSE64318

Protein-protein interaction network (PPI) analysis
Using the STRING online database and the Cytoscape software, a total of 128 DEGs of the 1578 DEGs were mapped into the PPI network complex. In this network, 32 nodes were chosen as hub nodes and included 2 TFs, 14 miRNAs, and 16 mRNAs; the results are presented in Table 4 and Figure 5. Moreover, we found that 6 mRNAs (i.e., EGFR, VEGFA, PIK3R1, DLG4, TGFBR1 and KIT) exhibited higher degrees (>10) and miRNA-mRNA pairs. These findings suggest that these nodes may play important roles in the development of PCa.

TCGA Datasets Analysis
The TCGA dataset analyses were performed to demonstrate that the aberrant expression of the hub genes, including EGFR, VEGFA, PIK3R1, DLG4, TGFBR1 and KIT, were significantly different between PCa and normal prostate tissues ( Figure   6).

Discussion
Prostate cancer (PCa) has become a public health issue of great concern worldwide [25]. However, the molecular mechanisms involved in the progress of PCa are still unclear. Therefore, it is very crucial to study the mechanism and to identify molecular targets for diagnosis and treatment. In this study, we performed a comprehensive bioinformatics analysis and retrieved the mRNAs, miRNAs, lncRNAs and TFs in the interaction network and revealed the key genes relevant to prostate cancer.
MiRNA expressions in tumour tissues often differ from that of normal tissue, and the differential expression affects the occurrence, development and prognosis of tumours [26,27]. Studies have confirmed that miRNA expression profiles can be used as biomarkers for the early detection, classification and prognosis of tumours [28,29].
lncRNAs act as miRNAs sponges and can regulate miRNAs abundance and compete with mRNAs for the binding of miRNAs [30]. By constructing a mRNA-miRNA-lncRNA network, we found that the aberrant expression of lncRNAs led to the abnormal expression of 5 miRNAs (i.e., has-miR-20a, has-miR-20b, has-miR-23b, has-let-7a and has-let-7d) in PCa and thus regulated the expression of the target mRNAs. A previous study demonstrated that 5 miRNAs regulate the development of prostate cancer by targeting specific mRNAs and play an important role in the regulation of prostate cancer [31,32,33]. The involvements of key lncRNAs including HYMAI, MEG3, IPO5P1, MAG12-AS3, RMST and TRG-AS1 are important. Among them, MEG3 is an important tumour suppressor gene that inhibits cell proliferation and induces apoptosis in PCa [34]. The finding of these lncRNAs suggests potential diagnostic and therapeutic targets for PCa.
Next, we conducted a functional enrichment analysis of the DEGs based on the mRNAs-miRNAs-lncRNAs network. We found that the DEGs were mainly enriched in the nucleus and cytoplasm, were involved in the regulation of transcription, were related to the sequence-specific DNA binding, and participated in the regulation of the PI3K-Akt signalling pathway; this pathway is related to cancer and the focal adhesion signalling pathway. To further analysed the key genes related to PCa, we constructed a PPI network. More significantly, we found that the transcription factor NR3C1 was a hub gene that participated in the regulation of the expression of multiple miRNAs (has-miR-20a, has-miR-20b, has-miR-23b). Puhr M et al assessed NR3C1 expression and the functional significance in tissues from PCa and found that it is a key factor for the development of PCa [35]. Moreover, we found that its regulatory function is similar to MEG3 and its down-regulation leads to increased expression of miRNAs (has-miR-20a, has-miR-20b, and has-miR-23b). Based on these results, we speculate that there may be some regulatory relationship between NR3C1 and MEG3.
Finally, through PPI and TCGA dataset analyses, we found that 6 mRNAs (i.e., EGFR, VEGFA, PIK3R1, DLG4, TGFBR1 and KIT) had higher degrees and miRNA-mRNA pairs. These expressions were lower and were significantly different between PCa and normal prostate tissues. EGFR belongs to a family of cell membrane receptor tyrosine kinases and is a key factor in tumour cell growth and invasion . We also found that 6 mRNAs (EGFR, VEGFA, PIK3R1, DLG4, TGFBR1 and KIT) were associated with the abnormal expression of 5 miRNAs (has-miR-20a, has-miR-20b, has-miR-23b, has-let-7a and has-let-7d) in PCa. In conclusion, the hub genes that we identified might play crucial roles in PCa.

Conclusion
We constructed and analysed mRNAs, miRNAs, lncRNAs, and TF interaction networks to reveal the key genes in prostate cancer. We found that 5 miRNAs (has-miR-20a, has-miR-20b, has-miR-23b, has-let-7a and has-let-7d), 6 lncRNAs PCa and normal tissues. Further research is needed to specify the molecular mechanism of these hub genes in PCa.
Li collected and analysed the data. Sheng-Yu Wang provided the analytical software.

Disclosure
The authors indicate no potential conflicts of interest related to this work.